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GGPA

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

Bioconductor version: 3.23 · Package version: 1.24.0

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GGPA")

Details

MaintainerDongjun Chung <dongjun.chung@gmail.com>
AuthorDongjun Chung, Hang J. Kim, Carter Allen
LicenseGPL (>= 2)
URLhttps://github.com/dongjunchung/GGPA/
System RequirementsGNU make
Downloads rank379
Source branchRELEASE_3_23
biocViewsClassification, Clustering, DifferentialExpression, GeneExpression, Genetics, GenomeWideAssociation, MultipleComparison, Preprocessing, SNP, Software, StatisticalMethod

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageGGPA_1.24.0.tar.gz
Windows binary (x86_64)GGPA_1.24.0.zip
macOS binary (arm64)GGPA_1.24.0.tgz
macOS binary (x86_64)GGPA_1.24.0.tgz
Dependencies

Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats

Imports: Rcpp (>= 0.11.3)

LinkingTo: Rcpp, RcppArmadillo

Suggests: BiocStyle